Systems, methods and apparatus for indexing and predicting wind power output from virtual wind farms

ABSTRACT

This disclosure describes systems, methods, and apparatus for predicting electrical power output from wind farms using statistical methods and measured wind speeds near boundaries of control volumes that encompass the wind turbines of interest. These systems, methods, and apparatus can provide electrical power output predictions of up to 6, 12, 24, or 48 hours in advance of actual power reaching the grid.

FIELD OF THE DISCLOSURE

The present invention relates generally to improved renewable energygeneration. In particular, but not by way of limitation, the presentinvention relates to systems, methods and apparatuses for forecastingregional wind power output and changes therein, in order to enhanceefficient wind power energy usage.

BACKGROUND

Efficient use of wind-generated electrical energy can be assisted byaccurate and sufficiently-early electrical power output forecasts forwind farms and regions of wind farms. Current forecasting methods do notgive much advanced warning and are prone to error when there aresubstantial changes in power output. In other words, current forecastingmethods can be ineffective when wind speeds and direction vary fromestablished trends. The inability to sufficiently predict dynamics inelectrical power output is in part due to the traditional use ofnumerical weather models, which are computationally-intensive and thusdo not model weather patterns fast enough to handle wind dynamics.

In particular, wind speed data is typically entered into a numericalweather model in order to predict future wind speeds. The predicted windspeeds are then converted to an estimated electrical power output foreach wind turbine in a given location or region by passing the datathrough a power curve tailored to the given wind turbines. However, themodeling is time-consuming and thus predictions tend to be inaccuratewhen predicting large changes in regional wind power output, especiallyon lead times shorter than 12 hours.

Accuracy is also hampered by the inability to obtain wind speed data atthe location of every wind turbine unless one owns the wind turbines.Given the limited locations that can be used to place wind speedsensors, typical electrical power output methods are fraught with theinaccuracies of predicting wind speeds at locations other than where thesensors are located.

SUMMARY

Exemplary embodiments of the present invention that are shown in thedrawings are summarized below. These and other embodiments are morefully described in the Detailed Description section. It is to beunderstood, however, that there is no intention to limit the inventionto the forms described in this Summary of the Invention or in theDetailed Description. One skilled in the art can recognize that therearc numerous modifications. equivalents and alternative constructionsthat fall within the spirit and scope of the invention as expressed inthe claims.

Some embodiments of the disclosure may be characterized as a method ofpredicting wind power output for one or more wind turbines. Measurementscan be made to generate a first set of wind speed data for a number ofwind speed sensors and at a number of times for each wind speed sensor.The first set of wind speed data can be converted to a first set ofelectrical power output data. Weights can be determined to assign toeach wind speed sensor based on the first set of electrical power outputdata along with published wind power output data for a regionencompassing the wind speed sensors. Via further measurements, a secondset of wind speed data can be generated for the wind speed sensors at anumber of further times. The second set of wind speed data can beconverted to a second set of electrical power output data and the secondset can be multiplied by the weights to generate weighted electricalpower output data. Finally, the weighted electrical power output datafor the plurality of wind speed sensors can be summed.

Other embodiments of the disclosure may also be characterized as asystem for forecasting electrical wind power output. The system caninclude a memory, a power conversion module, a weighting module, and asummation module. The memory can store first and second wind speed dataand data describing one or more power curves. The first wind speed datacan be measured for a first time series and can be measured by a numberof wind speed sensors arranged near a control volume boundary enclosingone or more wind turbines. The second wind speed data can be measuredfor a second time series by the same wind speed sensors. The powercurves can characterize one or more of the wind turbines oralternatively can characterize the entire control volume. The powerconversion module is configured to convert the first and second windspeed data into first and second electrical wind power output data. Theweighting module can determine a weight to assign to each wind speedsensor and multiplies each weight times a portion of the secondelectrical wind power output data. Specifically, the weights aremultiplied by electrical wind power output data corresponding to thewind speed sensor that corresponds to each weight. The result of thismultiplication is weighted electrical wind power output data. Finally,the summation module sums the weighted electrical wind power output datawhere the sum includes one weighted electrical wind power output valuefor each wind speed sensor.

Other embodiments of the disclosure can he characterized as anothermethod of forecasting electrical wind power output. Control volumes canbe selected where each encompasses one or more wind turbines. Wind speedsensors can be used to collect a first set of wind speed data, where thesensors are near a boundary of each of the control volumes. The firstset of wind speed data can be converted to a first set of electricalpower output data. Using statistical methods, a weight can be determinedand assigned to each wind speed sensor. These weights are determinedbased on the first set of electrical power output data. A second set ofwind speed data can also be collected via the same wind speed sensorsand this second set of data can be converted to a second set ofelectrical power output data. The second set of electrical power outputdata can be multiplied by the weights assigned to each wind speed sensorto generate a set of weighted electrical power output data. Lastly, theset of weighted electrical power output data can be summed.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects and advantages and a more complete understanding of thepresent invention are apparent and more readily appreciated by referringto the following detailed description and to the appended claims whentaken in conjunction with the accompanying drawings:

FIG. 1 illustrates one embodiment of a control volume encompassing windturbines from a plurality of wind farms.

FIG. 2 illustrates a regional view of three control volumes eachcomprising a plurality of wind farms.

FIG. 3 illustrates a method of measuring and predicting wind poweroutput.

FIG. 4 is a chart depicting exemplary actual electrical power output perpublished data along with an exemplary electrical power output indexgenerated according to a method of this disclosure.

FIG. 5 illustrates a system for forecasting electrical wind poweroutput.

FIG. 6 illustrates a machine such as a computer system in which thesystems and methods herein disclosed can be implemented.

DETAILED DESCRIPTION

FIG. 1 illustrates one embodiment of a control volume encompassing windturbines from a plurality of wind farms. The first, second, and thirdwind farms 102, 104, 106 are arbitrarily located and each comprises aplurality of wind turbines 108. The farms 102, 104, 106 are defined bythe locations of the turbines 108, and the dashed ellipses are merelyused to help distinguish the first, second, and third wind farms 102,104, 106. The wind farms 102, 104, 106 are centered around a center ofwind turbines 120 (the center 120 is an average of all wind turbine 108locations).

The plurality of wind turbines 108 are enclosed by a control volume 112.The control volume 112 has a boundary, and the wind speed sensors 114are located proximal to this boundary. The control volume 112 boundarytends to be further from the center of wind turbines 120 in an upwinddirection (lower left of FIG. 1) and closer to the center 120 in adownwind direction (upper right of FIG. 1), where the prevailing winddirection 110 is from the lower left of FIG. 1 to the upper right. Thedirections, shapes, and proportions of the elements described above aremerely illustrative and in no way meant to be limiting.

The wind speed sensors 114 can take a variety of forms and arrangements.In the illustrated embodiment, the wind speed sensors 114 are arrangedon support structures, and there can be one or more wind speed sensors114 fixed to each support structure. However, it should be understoodthat wind speed sensors 114 can also be located on the ground, onbuildings, attached to stationary vehicles, affixed to wind turbines(e.g., wind speed sensors 116), or supported in any other fashionimaginable. The wind speed sensors 114 can be arranged on the controlvolume 112 boundary or proximal to the boundary, where “proximal” caninclude within 10 km or within 100 km. Some wind speed sensors can belocated within wind farms 102, 104, 106, such as wind speed sensors 118.

Wind speed sensors 114 can be arranged at varying elevations rangingfrom ground height up to 1 km above the ground and including windturbine hub height. Two or more wind speed sensors 114 can be arrangedin the same location but at different elevations. A greater numberand/or density of wind speed sensors 114 can be arranged downwind of thecenter 120. In other words a greater number and/or density of wind speedsensors 114 can be arranged proximal to a downwind portion of thecontrol volume 112 boundary. Fewer wind speed sensors 114 can bearranged upwind of the center 120 proximal to an upwind boundary of thecontrol volume 112.

The control volume 112 can encompass one or more portions of multiplewind farms 102, 104, 106, can encompass a single wind farm, or canencompass one or a few wind turbines 108. In an embodiment, the controlvolume 112 can encompass wind turbines 108 from two or more wind farms102, 104, 106. The control volume 112 can have a curved or straightboundary, or a boundary comprising both curved and straight sections. Inthe illustrated embodiment, the boundary is elliptical, but this is inno ways meant to limit the various shapes that boundaries can take.

In an embodiment, the boundary can have an upwind portion and a downwindportion. The upwind and downwind portions can include more or less thanhalf the circumference of the boundary and the sum of the circumferencesof the upwind and downwind portions can be equal to or less than thecircumference of the entire boundary. The upwind portion can on averagebe further from a center of the wind turbines 120 than the downwindportion. As such, wind speed sensors 114 proximal to the upwind portionof the boundary can provide a prediction of wind speeds within thecontrol volume 112 while wind speed sensors 114 proximal to the downwindportion of the boundary can help correct the estimates of the upwindsensors 114 by measuring the wind speeds leaving the control volume 112.The wind speed sensors 114 proximal to the downwind portion of theboundary can also predict electrical power output when the wind travelsopposite to the prevailing wind direction.

In an embodiment, the wind speed sensors 114 on an upwind portion of theboundary can be within an upwind distance from the majority of turbines108 within the associated control volume 112, or from a center of thewind turbines 120. The upwind distance is a distance traversed by windtraveling at an average wind speed within the control volume 112, or anaverage wind speed for the region, or some other wind speed, for a setamount of time (e.g., 2, 4, 8, 12, 24 , or 48 hours). In other words,the wind speed sensors 114 can be arranged such that they measure a windspeed, on average, 2, 4, 8, 12, 24 , or 48 hours or less before themeasured portion of wind reaches a set of wind turbines 108 or a windfarm 102, 104, 106 within the control volume 112. Thus, the boundary canbe defined such that wind speed sensors 114 arranged on an upwindportion of the boundary can provide a prediction of wind speed, and thuselectrical power output, for wind turbines 108 within the control volume112. This prediction is more accurate, the closer the upwind portion ofthe boundary is to the wind turbines 108 and the center 120, but theprediction can be made further in advance in terms of time by moving theupwind portion of the boundary further upwind. A downwind portion of theboundary can be arranged from the center 120 at a distance that wouldtake an average wind speed no more than two hours to traverse. Thus, thewind speed sensors 114 downwind of the wind turbines 108 and wind farms102, 104, 106 can be closer to the wind turbines 108 and wind farms 102,104, 106 than the upwind wind speed sensors 114.

FIG. 2 illustrates a regional view of three control volumes eachcomprising a plurality of wind farms. In this illustration, the windturbine symbols each represent a wind farm 208 rather than an individualwind turbine. This view shows that a region can comprise a plurality ofcontrol volumes 202, 204, 206, and those control volumes 202, 204, 206can in some instances overlap. Each control volume 202, 204, 206 has aboundary and there are a plurality of wind speed sensors arrangedproximal to that boundary. The boundaries tend to be further from thewind farms 208 on an upwind side of the control volumes 202, 204, 206.

FIG. 3 illustrates a method of measuring and predicting wind poweroutput. The method 300 can make real-time estimates or forecast of anelectric power output, or changes therein, for all wind farms and windturbines in a region (or in a control volume). In an embodiment, themethod 300 can estimate large or significant changes in the electricpower output for all wind farms and wind turbines in a control volume.First, one or more control volumes are selected and a boundary of eachis defined in a select control volume operation 302. Wind speed data isthen collected from wind speed sensors arranged on or proximal to theboundary of each control volume in a first collect operation 304. In afirst convert operation 306, the wind speed data is converted to a firstset of estimated electrical power output by applying a power curve tothe wind speed data to produce electrical power output data. A weightingfactor for each sensor can then be statistically determined viadetermine operation 308. At some point, a second set of wind speed datais collected in a second collect operation 318. The second set of windspeed data is also converted to a second set of electrical power outputdata in a second convert operation 320. Multiplying the electrical powerof each wind speed sensor in the second set of electrical power outputdata by the weighting factors determined for each sensor gives aweighted electrical power output for each sensor in multiply operation310. Finally, a sum of the weighted electrical power output data foreach sensor is calculated in a sum operation 312 thus giving anestimated electrical power output for either a control volume or aregion encompassing one or more whole or partial control volumes.

Another way of looking at the method 300 is that each wind speed sensorrepresents a “virtual wind turbine” and the method 300 calculates aweighted electrical power that each virtual wind turbine contributes toan electrical power output of a control volume or region. Noteworthy isthe determination of weighting parameters based on power rather thanwind speed. In other words, determining weighting, or a wind speedsensor's statistical accuracy or contribution to a regional prediction,takes place after power conversion. Since power has a non-linearrelationship to wind speed, the method 300 produces more accurate andmore timely predictions than an embodiment where weighting is determinedfor wind speed and only later is the power conversion performed.

The control volume is selected in the select operation 302 in order toencompass a region of wind turbines and/or wind farms. The controlvolume can be a three dimensional space having a boundary (or controlsurface) encompassing wind turbines or wind farms whose predictedelectrical power output is of interest. By locating wind speed sensorsproximal to the boundary, wind activity within the control volume can beapproximated based on wind speed data from the wind speed sensors on theboundary of the control volume. In other words, measuring wind speedentering and leaving the control volume provides an accurate estimate ofwind speed within the control volume.

Given a control volume, an electrical power output for each wind speedsensor arranged proximal to the control volume boundary is determined,first by collecting wind speed data from wind speed sensors arrangedproximal to a boundary of the control volume and then by converting thewind speed data into estimated electrical power output data were a windturbine located in the same place (each wind speed sensor can be thoughtof as a virtual wind turbine or virtual wind farm). The wind speed datais collected in a collect first set of wind speed data operation 304.This first collect operation 304 can also involve collecting othermeteorological data such as temperature, air pressure, air density,humidity, etc. since these other atmospheric qualities can also be usedto more accurately convert wind speed to electrical power output. Thefirst collect operation 304 can collect these and other meteorologicaldata via a variety of methods including, but not limited to,conventional or propeller-based anemometers, resistance-basedtemperature and humidity sensors, SODARs, LIDARs, and specialty RADARs.Thus, the collect first set of wind speed data operation 304 can alsoinvolve collection of other meteorological data in addition to windspeed. The first set of wind speed data is collected over a period oftime such that each data point is associated with a time. Thus, thefirst set of wind speed data is a time series.

The first set of wind speed data can then be converted to a first set ofelectrical power output data in a first convert operation 306 using apower curve (or wind turbine power curve) to estimate the electricalpower output of a wind turbine located where each wind speed sensor is.Each power curve can be unique to a single wind turbine or to a make andmodel of wind turbine. Power curves can be provided by manufacturers orderived by users. Manufacturer power curves can even be modified inorder to better estimate the electrical power output of a virtual windturbine since there can be factors that the manufacture's power curvedoes not account for (e.g., a wind speed sensor located at 5 meters fromthe ground rather than hub height). The power curve can also depend onother meteorological factors, as mentioned above, such as temperature,humidity, air pressure, and air density (e.g., a wind turbine generatesmore power given the same wind speed but denser air).

While wind speed sensors provide accurate predictions of electricalpower output for virtual wind turbines, they are less accurate atpredicting the output of actual wind turbines which are usually locatedat a distance from the wind speed sensors. Ideally, one would place awind speed sensor on every wind turbine, but this would be prohibitivelyexpensive and often not feasible since those seeking to predictelectrical power Output from wind turbines are often not the sameentities that own the turbines. Hence the advantages of using controlvolumes and virtual wind turbines. By placing wind speed sensorsproximal to a boundary of a control volume, only a handful of wind speedsensors are needed to accurately predict the electrical power output ofhundreds if not thousands of wind turbines (however many can beencompassed by a control volume). A weighting operation based onstatistics and regression is used to assign a weighting factor to eachwind speed sensor, the weighting factor indicative of a statisticalcorrelation between a wind speed sensor's measurements and prior actualelectrical power output from wind turbines. In other words, theweighting factors indicate which wind speed sensors provide the mostaccurate predictions of electrical power output of wind turbines. Bydetermining and applying these weighting factors, new sets of wind speeddata can be collected and used to accurately predict electrical poweroutput from wind turbines despite the turbines being located dozens ifnot hundreds of miles away from the sensors and despite the fact thatthe number of turbines may greatly outnumber the wind speed sensors.

This weighting is performed by a determine operation 308. The determineoperation 308 can perform a fitting with known electrical power outputand measured wind speed data (or electrical power output data) as theinputs. In particular, a least squares fit of a weighted sum of thefirst set of electrical power output data (one value for each wind speedsensor or virtual wind turbine) can be used where the sum on the leftside of the equation is set equal to published electrical power outputdata (e.g., ERCOT electrical power output data). This weighted sum canbe written as follows:P _(t) =Σw _(j) p _(jt)  (Equation 1)

In Equation 1 , the sum or electrical power output P_(t) at time t (orestimated total electrical power output), equals the sum of weightedpower for a plurality of wind speed sensors or virtual wind turbines(denoted by the subscript j) at one or more times (denoted by thesubscript t). The sum is a time series comprising a plurality of values,each one corresponding to a different time (denoted by the subscript t).The electrical power output values p_(jt) have one value for every timein the time series. However, the weighting parameters w_(j) have onlyone value per sensor, and thus a single weighting parameter w_(j)applies to the entire time series for a given sensor. In other words,the number of weighting, factors w_(j) is equal to j and the number ofelectrical power output values p_(jt) is equal to j multiplied by t. Inthe determine operation 308, the weighting parameters w_(j) are unknown,and a fitting algorithm such as least squares is used to solve for theweighting parameters w_(j). This fitting can be performed for one ormore control volumes depending on the region of interest for predictingelectrical power output.

Once the weighting factors have been determined, they can be substitutedinto Equation 1 along with a new set of (or second set of) of electricalpower output data in a multiply operation 310 and used to predictelectrical power output P_(t) for the one or more control volumes in asum operation 312.

The second set of wind speed data is collected in a second collectoperation 318 and converted to a second set of electrical power outputdata in a second convert operation 320. The second collect operation 318and the second convert operation 320 can follow the first collectoperation 304, or overlap with at least part of the first collectoperation 304. The second collect and convert operations 318, 320 canalso take place after the first collect operation 304, and can overlapwith either or both of the first convert operation 306 and the determineoperation 308.

In a sum operation 312, the product of the weighting factors w_(j) anddata from the second set of electrical power output data are added in asummation to produce an estimated total electrical power output for theregion encompassing all wind speed sensors used in the summation. Thesum operation 312 can be performed for wind speed sensors in a singlecontrol volume or in multiple control volumes. The sum includes all windspeed sensors used in determining the weighting factors w_(j).

Depending on the locations of the sensors and the control volumes used,this sum can provide predictions of estimated total electrical poweroutput for a region of wind turbines or wind farms or control volumeswith different degrees of advanced warning. For instance, the estimatedtotal electrical power output P_(t) can be used as a time-dependentindex to show changes in electrical power output for a region (see forexample, FIG. 4). The index shows the estimated total electrical poweroutput P_(t) in advance of the actual electrical power reaching thegrid. The sum may provide an advanced prediction of electrical poweroutput that can he expected from a region enclosed by a control volumeor one or more regions downwind of the one or more control volumes. Theprediction time can vary between 0 and 48 hours and can be adjustable.For instance, the prediction time may be adjustable in fifteen-minuteincrements.

Once the sum operation 312 determines an estimated total electricalpower output for the plurality of sensors (or for a control volume orregion), the method 300 can update the weighting factors w_(j). To dothis a set of wind speed data is again collected that can be used todetermine weighting factors. In an embodiment, the second set of windspeed data can be used. However, in other instances a new set of windspeed data, a third set of wind speed data, can be collected. Eitherway, the method 300 resumes with the first collection operation 304(where the second or third set of wind speed data can be substituted forthe first set) and progresses as described above with the secondcollection operation 318 using the third set of data or a fourth set ofdata instead of the second set of data.

In one variation, the method 300 can be expedited by removing terms fromthe weighted sum (Equation 1) where the weighting factor is below athreshold (e.g., 0.1 or 0.01). Such terms can be neglected since thecontribution after weighting would be negligible.

The absence of a need to rely on high-performance computing used in themethod 300 contributes to the speed and accuracy of the prediction, aswell as the ability to anticipate large fluctuations which conventionalweather models cannot. The independence from high-performancecomputing-based modeling allows a wind power output prediction to bemade much faster since the few calculations involved in the method 300are less time consuming than modeling traditionally used to predict windspeeds.

In an embodiment, electrical power output for a plurality of windturbines is predicted via a statistical prediction. Traditional methodsmay measure wind speed, input the measurements to a numerical weatherprediction model (a set of equations, such as partial differentialequations, used to predict future pressure, air density, temperature,and/or wind velocity at one or more locations and altitudes) to predictfuture wind speeds, and then convert the predicted wind speeds topredicted electrical power output for wind turbines in the vicinity ofthe predicted wind speeds. In contrast, the statistical predictionherein disclosed measures a first set of wind speed data, converts thefirst set of wind speed data to a first set of electrical power outputdata, determines a statistical correlation between the first set of windspeed data and a cumulative electrical power output for wind turbineswhose electrical power output is to be predicted, measures a second setof wind speed data, converts the second set of wind speed data to asecond set of electrical power output data, and uses the second set ofelectrical power output data with the statistical correlation to predictelectrical power output for the wind turbines.

In particular, determining the statistical correlation involvesdetermining a weight to assign to each wind speed sensor where theweight represents a correlation between a wind speed measurement foreach sensor and the cumulative electrical power output for the windturbines whose electrical power output is to be predicted. In otherwords, wind speed sensors that measure wind speed that is a goodpredictor of electrical power output are assigned greater weights. Thisdetermination can be carried out via a fitting such as a least squaresfitting algorithm. The weights can then be multiplied by the second setof electrical power output data, and a sum of the weights times thesecond set of electrical power output data is found. This sum, which canbe referred to as an index, represents a 0-48 hour advanced predictionof electrical power output for the wind turbines.

FIG. 4 is a chart 400 depicting exemplary actual electrical power output404 per published data along with an exemplary electrical power outputindex 402 generated according to a method of this disclosure. The chart400 displays electrical power output in megawatts (y-axis) as a functionof time in hours (x-axis). The electrical power output index 402 is atime series of the total estimated electrical power output P_(t) asdiscussed with regards to FIG. 3 and Equation 1. The actual electricalpower output 404 represents data generated by the electrical gridoperator(s) and published. As seen, the index 402 and the actual output404 track very closely for a while and then the power drops off with theindex 402 predicting the power drop by a prediction time 406 (e.g., 6,12, 24 , or 48 hours).

FIG. 5 illustrates a system 500 for forecasting electrical wind poweroutput for one or more wind turbines 522, wind farms, or a region ofwind farms. The system can include a memory 502 storing a first windspeed data 504, a second wind speed data 506, and power curve data 508.The system 500 can also include a power conversion module 510, aweighting module 512, and a summation module 514.

The first wind speed data 504 can store wind speeds for a first timeseries (e.g., wind speeds measured between a first time and a secondtime in periodic increments). The first wind speed data 504 can thus bestored as discrete data points or as a vector. The first wind speed data504 can also include further meteorological data such as wind direction,humidity, temperature, air density, air pressure, etc. The first windspeed data 504 is measured by a plurality of wind speed sensors incommunication with the system 500 via a wired or wireless communicationpath 518. Two such wind speed sensors 516 are illustrated on a singlesupport. The wind speed sensors can be arranged proximal to a controlvolume boundary 520 enclosing one or more wind turbines 522.

The second wind speed data 506 can store wind speeds for a second timeseries (e.g., wind speeds measured between a third time and a fourthtime in periodic increments). The second wind speed data 506 can thus bestored as discrete data points or as a vector. The second wind speeddata 506 can also include further meteorological data such as winddirection, humidity, temperature, air density, air pressure. etc. Thesecond wind speed data 506, like the first wind speed data 504, ismeasured by the plurality of wind speed sensors, only two of these windspeed sensors 516 are illustrated. The second wind speed data 506 canoverlap with the first wind speed data 504. For instance, the secondwind speed data 506 can be the same data as the first wind speed data504, in other words a second set of measurements need not be made.Alternatively, some of the first wind speed data 504 can be used topopulate a portion of the second wind speed data 506 while the rest ofthe second wind speed data 506 is measured at times other than when thefirst wind speed data 506 is measured.

Power curve data 508 can describe a power curve of the one or more windturbines 522 in the control volume 520. Alternatively, the power curvedata 508 can describe a power curve for the control volume 520. Forinstance, where different wind turbines 522 operate within the samecontrol volume 520, it may be preferable to use a single power curve torepresent the entire control volume 520, where the power curve is anaverage power curve of the power curves for the individual wind turbines522. Alternatively, the power curve for the control volume 520 can besome other modification of the power curves for the individual windturbines 522 other than an average (e.g., a weighted average).

The power conversion module 510 can be configured to convert the firstand second sets of wind speed data 504, 506 into first and secondelectrical wind power output data. The power conversion module 510performs this conversion using the power curve described by the powercurve data 508.

The weighting. module 512 determines a weight to assign to each windspeed sensor and multiplies each weight times a portion of the secondelectrical wind power output data associated with each weight to produceweighted electrical wind power output data. In other words, for eachsecond electrical wind power output data point, the weighting module 512multiplies a weight times that data point, where the weight isdetermined for the wind speed sensor that generated the data point.These multiplied terms are then summed by the summation module 514.

The system 500 can be embodied in a computing device such as astandalone personal computer, a laptop, a server, or an embedded system.The system 500 can be embodied in hardware, software, firmware, or acombination of these.

FIG. 6 illustrates a machine such as a computer system in which thesystems and methods herein disclosed can be implemented. The systems andmethods described herein can be implemented in a machine such as acomputer system in addition to the specific physical devices describedherein. FIG. 6 shows a diagrammatic representation of one embodiment ofa machine in the exemplary form of a computer system 600 within which aset of instructions for causing a device to perform any one or more ofthe aspects and/or methodologies of the present disclosure to beexecuted. Computer system 600 includes a processor 605 and a memory 610that communicate with each other, and with other components, via a bus615. Bus 615 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 610 may include various components (e.g., machine readable media)including, but not limited to, a random access memory component (e.g., astatic RAM “SRAM”, a dynamic RAM “DRAM”, etc.), a read only component,and any combinations thereof In one example, a basic input/output system620 (BIOS), including basic routines that help to transfer informationbetween elements within computer system 600, such as during start-up,may be stored in memory 610. Memory 610 may also include (e.g., storedon one or more machine-readable media) instructions (e.g., software) 625embodying any one or more of the aspects and/or methodologies of thepresent disclosure. In another example, memory 610 may further includeany number of program modules including, but not limited to, anoperating system, one or more application programs, other programmodules, program data, and any combinations thereof.

Computer system 600 may also include a storage device 630. Examples of astorage device (e.g., storage device 630) include, but are not limitedto, a hard disk drive for reading from and/or writing to a hard disk, amagnetic disk drive for reading from and/or writing to a removablemagnetic disk, an optical disk drive for reading from and/or writing toan optical media (e.g., a CD, a DVD, etc.), a solid-state memory device,and any combinations thereof. Storage device 630 may be connected to bus615 by an appropriate interface (not shown). Example interfaces include,but are not limited to, SCSI, advanced technology attachment (ATA),serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 630 may beremovably interfaced with computer system 600 (e.g., via an externalport connector (not shown)). Particularly, storage device 630 and anassociated machine-readable medium 635 may provide nonvolatile and/orvolatile storage of machine-readable instructions, data structures,program modules, and/or other data for computer system 600. In oneexample, software 625 may reside, completely or partially, withinmachine-readable medium 635. In another example, software 625 mayreside, completely or partially, within processor 605. Computer system600 may also include an input device 640. In one example, a user ofcomputer system 600 may enter commands and/or other information intocomputer system 600 via input device 640. Examples of an input device640 include, but are not limited to, an alpha-numeric input device(e.g., a keyboard), a pointing device, a joystick, a gamepad, an audioinput device (e.g., a microphone, a voice response system, etc.), acursor control device (e.g., a mouse), a touchpad, an optical scanner, avideo capture device (e.g., a still camera, a video camera), touchscreen, and any combinations thereof. Input device 640 may be interfacedto bus 615 via any of a variety of interfaces (not shown) including, butnot limited to, a serial interface, a parallel interface, a game port, aUSB interface, a FIREWIRE interface, a direct interface to bus 615, andany combinations thereof.

A user may also input commands and/or other information to computersystem 600 via storage device 630 (e.g., a removable disk drive, a flashdrive, etc.) and/or a network interface device 645. A network interfacedevice, such as network interface device 645 may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 650, and one or more remote devices 655 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card, a modem, and any combinationthereof. Examples of a network or network segment include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a direct connection between two computingdevices, and any combinations thereof. A network, such as network 650,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 625,etc.) may be communicated to and/or from computer system 600 via networkinterface device 645.

Computer system 600 may further include a video display adapter 660 forcommunicating a displayable image to a display device, such as displaydevice 665. A display device may be utilized to display any numberand/or variety of indicators related to pollution impact and/orpollution offset attributable to a consumer, as discussed above.Examples of a display device include, but are not limited to, a liquidcrystal display (LCD), a cathode ray tube (CRT), a plasma display, andany combinations thereof. In addition to a display device, a computersystem 600 may include one or more other peripheral output devicesincluding, but not limited to, an audio speaker, a printer, and anycombinations thereof. Such peripheral output devices may be connected tobus 615 via a peripheral interface 670. Examples of a peripheralinterface include, but are not limited to. a serial port, a USBconnection. a FIREWIRE connection, a parallel connection, and anycombinations thereof. In one example an audio device may provide audiorelated to data of computer system 600 (e.g., data representing anindicator related to pollution impact and/or pollution offsetattributable to a consumer).

In conclusion, the present invention provides, among other things, amethod, system, and apparatus that enables real-time predictions ofelectrical power output from wind turbines via use of remotely-locatedwind speed sensors. Those skilled in the art can readily recognize thatnumerous variations and substitutions may be made in the invention, itsuse, and its configuration to achieve substantially the same results asachieved by the embodiments described herein. Accordingly, there is nointention to limit the invention to the disclosed exemplary forms. Manyvariations, modifications, and alternative constructions fall within thescope and spirit of the disclosed invention.

1. A method of predicting electrical wind power output for one or morewind turbines comprising: generating, via measurement, a first set ofwind speed data for a plurality of wind speed sensors and at a pluralityof times for each wind speed sensor; converting the first set of windspeed data to a first set of electrical power output data; determiningweights to assign each wind speed sensor based on the first set ofelectrical power output data and published wind power output data for aregion encompassing the plurality of wind speed sensors; generating, viameasurement, a second set of wind speed data for the plurality of windspeed sensors and at a plurality of times for each wind speed sensor;converting the second set of wind speed data to a second set ofelectrical power output data; multiplying the second set of electricalpower output data by the weights to generate weighted electrical poweroutput data; summing the weighted electrical power output data for theplurality of wind speed sensors.
 2. The method of claim 1, wherein bothconverting operations further comprise applying one or more powercurves, where each power curve represents at least one of the windturbines.
 3. The method of claim 2, wherein at least one, of the powercurves is modified from a power curve provided by a manufacturer of theone or more wind turbines.
 4. The method of claim 3, wherein the powercurve is representative of two or more wind turbines, where the two ormore wind turbines each have different power curves.
 5. The method ofclaim 1, wherein the first and second sets of wind speed data at leastpartially overlap.
 6. The method of claim 1, wherein the plurality ofwind speed sensors are arranged proximal to boundaries of one or morecontrol volumes enclosing one or more wind turbines or wind farms. 7.The method of claim 6, wherein for each control volume, a greaterportion of the control volume is upwind of wind turbines within thecontrol volume than a portion of the control volume downwind of thosewind turbines.
 8. The method of claim 7, wherein a majority of the windspeed sensors are arranged proximal to an upwind portion of theboundary.
 9. The method of claim 1, further comprising predictingelectrical power output from a region encompassing the plurality of windspeed sensors.
 10. The method of claim 9, wherein the predictingprecedes actual electrical power output by 0-48 hours.
 11. The method ofclaim 1, wherein at least some of the plurality of wind speed sensorsare arranged at a wind turbine hub height.
 12. The method of claim 1,wherein at least some of the plurality of wind speed sensors arearranged above a wind turbine hub height.
 13. A system comprising: amemory For storing: first wind speed data for a first time series,measured by a plurality of wind speed sensors each arranged proximal toa control volume boundary enclosing one or more wind turbines; andsecond wind speed data for a second time series, measured by theplurality of wind speed sensors; data describing one or more powercurves, where each power curve characterizes one or more of the windturbines or characterizes the control volume; a power conversion moduleconfigured to convert the first and second wind speed data into firstand second electrical wind power output data; a weighting module that:determines a weight to assign to each wind speed sensor; and multiplieseach weight times a portion of the second electrical wind power outputdata associated with each weight to produce weighted electrical windpower output data; and a summation module that sums the weightedelectrical wind power output data for the plurality of wind speedsensors.
 14. The system of claim 13, wherein the weight is an updatedweight.
 15. A method comprising: selecting a plurality of controlvolumes each encompassing one or more wind turbines; collecting a firstset of wind speed data from wind speed sensors proximal to a boundary ofeach of the plurality of control volumes and converting the first set ofwind speed data to a first set of electrical power output data;statistically determining a weight to assign to each wind speed sensor,given the first set of electrical power output data; collecting a secondset of wind speed data from the wind speed sensors and converting thesecond set of wind speed data to a second set of electrical power outputdata; multiplying the second set of electrical power output data by theweights assigned to each wind speed sensor to generate a set of weightedelectrical power output data; and summing the set of weighted electricalpower output data.
 16. The method of claim 15, wherein at least one ofthe control volumes encompasses a wind farm.
 17. The method of claim 15,wherein at least one of the control volumes encompasses a portion of twoor more wind farms.
 18. The method of claim 15, wherein equivalentweights are assigned to wind speed sensors associated with the samecontrol volume.
 19. The method of claim 15, further comprising,periodically updating the weights by repeating the collecting a firstset of wind speed data and the statistically determining steps.
 20. Themethod of claim 19, wherein the second set of wind speed data overlapsat least partially with the first set of wind speed data.